Engineering Commons^™

Flexural Strength Of (Hf,Nb,Ta,Ti,Zr)B2–(Hf,Nb,Ta,Ti,Zr)C High-Entropy Dual-Phase Ceramics, Rubia Hassan, William G. Fahrenholtz, Gregory E. Hilmas, Jeremy Watts

Materials Science and Engineering Faculty Research & Creative Works

The room and elevated temperature flexural strengths were measured for (Hf,Nb,Ta,Ti,Zr)B2–(Hf,Nb,Ta,Ti,Zr)C high-entropy dual-phase ceramics. The fracture of ceramics originated from two distinct flaw populations that were either surface defects introduced during specimen preparation or volume defects introduced during processing. The average strength at room temperature was 638 ± 94 MPa with individual bars strengths as high as ∼800 MPa. At 1800°C, average strength increased to 786 ± 265 MPa with individual bar strengths as high as ∼1050 MPa. Above 1800°C, creep dominated resulting in deformation of the bars without fracture. The fracture mode changed from completely trans granular at room …

Optimization Of Persulfate Activation System Using Ferrous Sulfate For Toc Removal From Real Domestic Wastewater, Mohamed Fouad, Mohamed Mossad, Mohamed Gar Alalm, Mahmoud Samy

Mansoura Engineering Journal

The reuse of treated wastewater is increasingly recognized as a sustainable solution to address global water shortage. This study investigates the effectiveness of a persulfate-based advanced oxidation process (AOP) using sodium persulfate (Na2S2O8) activated by ferrous sulfate (FeSO4) as a tertiary treatment for secondary effluents. Real municipal wastewater was collected from Agouzain Wastewater Treatment Plant (WWTP) in Kafr El-Sheikh, Egypt. A Box Benkhen design (BBD) was employed to optimize three key variables: FeSO4 dose (0.5–2.0 g/L), Na2S2O8 dose (0.5–2.0 g/L), and pH (3.0–9.0), with total organic carbon (TOC) removal as the main response. Under optimized conditions (0.50 g/L FeSO4, 2.00 …

Introducing Sustainable Development Goals In College Curricula: A Way Forward, Christy Ashley, Jason D. Oliver, Hillary Leonard

Markets, Globalization & Development Review

The commentary proposes a blueprint to help guide curriculum innovations, partnerships, and teaching interventions that incorporate the United Nations sustainable development goals (SDGs) into the curriculum. It suggests the utilization of AACSB’s Societal Impact Canvas with Ancona et al.’s (2007) Leadership Capabilities (Sensemaking, Relating, Visioning, Inventing) to provide a blueprint for mission-aligned SDG integration at a local level. It provides an illustrative example from the University of Rhode Island (USA), where the focus is on the Blue Economy. It aims to provide practical guidance for how a college or university can efficiently organize to gain stakeholder input that helps enhance …

A Large Electroencephalogram Database Of Freewill Reaching And Grasping Tasks For Brain Machine Interfaces, Bhoj Raj Thapa, John Boggess, Jihye Bae

Electrical and Computer Engineering Faculty Publications

Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects’ freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and …

Burnout At University: Cross-Sectional Study Among Dental Students, Manal Elhijazi, Hajar Nafia, Ihsane Benyahya

Health Sciences

Context: The university constitutes a demanding and competitive learning environment which can impact the well-being of students. The objective of this survey is to assess the burnout of students at the faculty of dentistry.

Materials and methods: A cross-sectional study was conducted via an anonymous questionnaire with 3 sections: socio-demographic data, lifestyle and habit, and burnout through the Maslach burnout inventory-student survey (MBI-SS) . Continuous variables were presented as means and standard deviations,categorical variables as numbers and percentages. The variables were tested in univariate analysis to assess predictive factors related to burnout using the Khi-2 test. The significance level was …

Effective Management Of Disused Sealed Radioactive Sources In Uganda, Susan Nalumansi

International Journal of Nuclear Security

Sealed radioactive sources are used in a wide range of applications in Uganda, such as medicine, agriculture, geology, industry, and other fields. After a given useful period, the sealed sources can no longer perform their intended purpose and are thus regarded as disused. However, depending on the activity of the source, the disused sources are highly active and can cause significant damage to human health and the environment when mismanaged. A regulatory body was established with the mandate to regulate peaceful applications of ionizing radiation in Uganda. Given the vulnerability of sealed sources at the end of their life cycle …

Development Of Stalling Detection Algorithms And Operator Behavior Models For Evaluating Hydraulic Excavator Performance, Mateo Fernando Montenegro Defaz

Masters Theses

This study aims to develop a reliable algorithm for stalling detection and investigate how operator behaviors impact the truck-loading process, focusing on swing and bucket rotations. While the study of excavator digging conditions and equipment performance is well understood, describing stalling from telemetry data and the influence of operators’ behavior on overall system efficiency remain underexplored. This study seeks to: (i) develop a reliable algorithm for stall detection by analyzing key operational variables such as velocity and angles; (ii) implement statistical and machine learning techniques, specifically a Support Vector Machine (SVM) classification algorithm, to differentiate between ideal and non-ideal digging …

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …

Axial Flux Permanent Magnet Generators For Direct-Drive Wind Turbines - Review And Optimal Design Studies, Matin Vatani, Ali Mohammadi, Donovin D. Lewis, John F. Eastham, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a comprehensive analysis of the feasibility, cost, and electromagnetic performance of five distinct types of axial flux permanent magnet (AFPM) generators designed for direct-drive wind turbines. The generator configurations investigated include a single-sided AFPM generator with a surface-mounted PM rotor (AFPMG), a double-sided AFPM generator featuring PMs on the stator and a reluctance rotor (AFPMG-RR), a coreless stator AFPM generator with surface PMs (CAFPMG-SPM), and a coreless stator AFPM generator with a Halbach PM array rotor (CAFPMG-Hal). Each generator’s operating principles and configurations are thoroughly explained and compared. Large-scale multi-objective design optimizations were conducted on each type, …

Review Of Coreless Axial Flux Permanent Magnet Machines With Applications Including Electric Aircraft Propulsion, Matin Vatani, Donovin D. Lewis, John F. Eastham, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial flux permanent magnet (AFPM) machines with entirely or stator coreless configurations offer significant advantages, including low rotational losses, the absence of cogging torque, minimal torque ripple, and the potential for high specific power density. Advances in permanent magnet (PM) technology and cooling techniques have enabled fully coreless AFPM machines to achieve high magnetic and current loadings without core saturation, making them suitable for high performance applications like electric aircraft propulsion. This paper reviews recent advancements in coreless AFPM machines, tracing their historical and chronological development. Various topologies, including single- and double-sided and multi-disk designs, are examined to identify the …

Speed Variation Based Power Regulation Concept For Dynamic Wireless Charging, Donovin D. Lewis, Sudarshan T. Harave, Omer Onar, Mostak Mohammad, Veda Prakash Galigekere, Gui-Jia Su, Dan Ionel

Electrical and Computer Engineering Graduate Research

On-road wireless charging of electric vehicles (EVs) in-motion could potentially reduce range anxiety or battery size with wide-spread deployment. The planning and implementation of such systems are greatly complicated due to their susceptibility to load variation inherent to traffic flow. This paper proposes a method for derisking the potential for traffic slowdowns by compensating for reduced vehicle speed and investigates how implementation may affect system performance. A load modeling case study is presented at 200kW for a mile of high-speed roadway employing speed-based power regulation with results indicating average power usage and maximum car hosting capability can be reduced by …

Fault-Tolerant High-Power Two-Phase Coreless Afpm Motor-Drive System Concept, Yaser Chulaee, Ali Mohammadi, Matin Vatani, Aaron M. Cramer, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper proposes a fault-tolerant, high-power two-phase coreless axial flux permanent magnet (AFPM) motor drive system concept. Initially, the performance of a two-phase variant of the coreless AFPM machine is compared with that of its three-phase counterpart using both 3D finite element analysis (FEA) and experiments. Thanks to the intrinsic unique features of coreless AFPM machines, the analysis of the results demonstrates that the two-phase configuration has comparable power density (kW/kg) and efficiency. In terms of fault tolerance, the two-phase configuration exhibits superior performance, as the mutual inductance between phases is zero, resulting in an electric machine with magnetically decoupled …

Two-Level Multi-Objective Design Optimization Including Torque Ripple Minimization For Stator Excited Synchronous And Flux Switching Machines, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Electric machine performance can be enhanced through torque ripple mitigation, which reduces mechanical vibrations and improves system stability and operation. This paper proposes a novel two-level optimization method for synchronous flux-switching and hybrid excitation machines, featuring an innovative multi-point spline shaping approach for torque ripple minimization. The study employs experimentally validated models based on a prototype with a similar topology. The optimization process is implemented on two distinct designs: a 20-pole inner rotor PM-excited stator machine and a 28-pole outer rotor DC-excited stator machine. Analysis results demonstrate that the proposed innovative method significantly reduces torque ripple in both configurations. Force …

Electromagnetic Design Characterization Of Synchronous Machines With Flux Switching Effect Employing Reluctance Rotors And Stators With Pms And Ac Concentrated Coils, Oluwaseun A. Badewa, Ali Mohammadi, Donovin D. Lewis, Somasundaram Essakiappan, Madhav Manjrekar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper introduces an innovative design for a synchronous electric motor that has phase coils and permanent magnets (PM) embedded in the stator. The electric machine contains both concentrated winded phase coils in dedicated slots and spoke-type permanent magnets solely in the stator. The spoke-type magnet arrangement achieves a high flux intensity even with low remanence non-rare earth permanent magnets. As the rotor has no active electromagnetic components, the risk for demagnetization is reduced and advanced cooling applied to the stator can enable high-speed operation. The principle of operation and polarity of the motor are investigated with a unique approach …

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Cca Analysis Using Computer Vision Techniques, Rahul Thakur

Master's Projects

Coral reefs are an essential part of the marine ecosystem. They perform a wide variety of tasks, some directly and others indirectly. They can produce oxygen, absorb carbon dioxide, along with supporting ocean habitat. Crustose Coralline Algae (“CCA”) plays an important role in helping provide structural support to Coral Reef ecosystems. However, global warming is causing ocean water to become more acidic resulting in coral bleaching. This is leading to changes in coral environments and causing coral deaths at alarming rates. Object detection using computer vision techniques, specifically deep learning, can help to monitor coral reef health and identify CCA …

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Smart: Semantic Mapping And Analysis For Regional Terrain Using Multi-Scale U-Net And Topsis, Rashmi Sonth

Master's Projects

Accurate land use classification is the backbone for urban planning. But with poor quality satellite images, varied landscapes and structures which are changing faster than ever, it becomes a challenge to define clear boundaries and hence to urban planning. This research explores the application of deep-learning model for land use classification and asses the suitability of the land. The proposed model combines a multi-scale U-Net architecture with Transformer blocks applied on a multi-spectral satellite images that improves the semantic segmentation greatly across the urban and rural regions. Additionally, a patch-wise segmentation is applied to overcome the common problem of feature …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy

Master's Projects

The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …

Quantifying Deviation Of Elasticity In The Quasi-Elastic Domain Of Granular Soils, Wen Deng, Yufan Jin, Pingxin Xia, Qingqing Fu, Xiong Xia, Xiaoxia Guo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The absence of purely elastic properties in granular soil is attributed to the reversible sliding that occurs between particles during the loading–unloading cycle, leading to the dissipation of energy through friction. Instead of the strictly elastic domain, we opt for a quasi-elastic domain to examine elastic behavior within the confines of elastic theory. Within this quasi-elastic realm, the response deviates to some extent from the idealized pure elastic behavior posited by the elastic theory. As a result, the elastic constitutive relations applied to granular materials are no longer strictly accurate and may exhibit deviations. With the aim of exploring the …

Extending The Applicability Of Thermal Protection System Ballistic Limit Equations Beyond The Testable Regime, William P. Schonberg, Michael D. Squire

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Spacecraft use thermal insulation in their design, which is usually placed on their exterior. As such, it is susceptible to high-speed impacts by meteoroids and orbital debris, which can damage it to a point where the protection it offers is below acceptable limits. It is important to be able to characterize expected levels of damage stemming from such high-speed impacts. In this paper, we extend the applicability of a previously developed ballistic limit equation (BLE) for one such thermal insulation material using the results of a series of Smooth Particle Hydrodynamics Code (SPHC) impact simulations at velocities in excess of …

Florida Statewide Coastal Restoration Guide, Melinda J. Donnelly, Matthew Martin, Christen Fleming, Kelly Kibler, Linda Walters, Patrick Shearer, Lauren Jennison, Erika Lin, George Zaragoza

Faculty Scholarship and Creative Works

The Statewide Coastal Restoration Guide (SCRG) is a comprehensive planning resource developed through a collaboration between Florida Department of Environmental Protection (FDEP), University of Central Florida (UCF), and Resource Environmental Solutions (RES) to strengthen and unify shoreline restoration efforts across Florida. With over 4,500 miles of estuarine shoreline, Florida’s coasts are vital to the state’s ecological health, economic prosperity, and community resilience. However, they face increasing threats from sea level rise, stronger storms, erosion, habitat degradation, and development. The SCRG responds to these challenges by providing science-based, non-regulatory guidance for implementing resilient, nature-based restoration strategies that protect both ecosystems and …

Conference Report Of Wmu Maritime Affairs Conference 2025 : Transforming A Maritime Future : Digitalization And Decarbonization, World Maritime University

Conference Papers

No abstract provided.

Empirical Study Of Far-Field Radio Frequency Wireless Power Transfer Testbed For Underground Mines Based On 1.8 Ghz, Anabi Hilary Kelechi, Samuel Frimpong

Mining Engineering Faculty Research & Creative Works

A novel 1.8 GHz far-field wireless power transfer (WPT) testbed was designed to achieve long-distance energy harvesting in underground mine (UM). Prior experimental studies confirm that underground mines experience 50 dB radio frequency (RF) signal attenuation compared to a typical wireless environment. Due to this severe signal attenuation, the currently proposed radio frequency energy harvesting (RF-EH) testbed cannot be deployed in underground mines. In this study, we proposed, designed and performed an empirical study with a military-grade class A radio frequency wireless power transfer (RF-WPT) testbed. The various digital modulation schemes experimented with included: BPSK, DBPSK, QPSK, OQPSK, Pi4DQPSK, 8PSK, …

Anisotropic And Temperature-Tunable Second-Harmonic Vortex Generation In Ferroelectric Nbocl2 Holograms, Jayanta Deka, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Optical vortex beams with helical phase fronts have immense potential to enhance data capacity due to the unbounded values of orbital angular momentum. Chip-scale platforms for producing vortex beams are of paramount importance for a variety of applications. On the other hand, 2D materials with unique optical properties are essential for developing multifunctional ultrathin photonic devices. Here, anisotropic and temperature-tunable second-harmonic vortex beam generation is demonstrated with ultrathin ferroelectric niobium oxide dichloride (NbOCl₂) fork holograms. The polarization-resolved Raman measurements are performed on the NbOCl₂ crystal to understand the anisotropic behavior of the Raman modes. It is demonstrated …

Machine Learning Approach For Defect Prediction In Metal 3d Printing For Aerospace Applications, Yerlik Gabdulla, Md Hazrat Ali, Frank Liou, Essam Shehab

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additive manufacturing (AM) has revolutionized the aerospace industry by enabling the production of lightweight and high-strength components, such as aerospace engine components and structural elements. The ability to create complex geometries and reduce material waste is particularly beneficial for aerospace applications, where performance and weight reduction are paramount. However, ensuring the quality and reliability of these components remains a challenge, particularly in mass production, which is related to material quality, expensive processes, and longer computational times than conventional manufacturing methods. This paper proposes an approach utilizing a Decision Tree Classification Machine Learning Algorithm to predict the possibility of defect occurrence …

In-Situ Transmission Electron Microscopy Investigation Of Grain Size And Temperature Dependent Irradiation Behavior Of 304l Stainless Steel, Anish Ranjan, Matthew Luebbe, Nastaran Motaharinia, Wei Ying Chen, Frank Liou, Haiming Wen

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The influence of grain size, irradiation temperature, and dose on the evolution of irradiation-induced defects in austenitic 304L stainless steel (SS) was systematically investigated. Coarse-grained (CG), ultrafine-grained (UFG), and nanocrystalline (NC) specimens were exposed to irradiation doses up to 10 displacements per atom (dpa) at room temperature (RT), 300°C, and 500°C. Dislocation loop size and density were quantitatively analyzed using transmission electron microscopy, and results showed that the dislocation loop size remained comparable across different grain sizes. However, loop density was strongly dependent on the grain size. The CG specimens exhibited the highest loop density due to a limited fraction …

A Framework For Fair And Trustworthy Multimodal Video Anomaly Detection: Fusion, Dataset Quality Auditing, Explainability, And Fairness, Omeshamisu Judith Anigala

Electronic Theses and Dissertations

No abstract provided.